Developing Convolutional Neural Network for Recognition of Bone Fractures in X-ray Images

• Autorzy: Saad Aymen , Sheikh Usman Ullah , Moslim Mortada Sabri

Identyfikatory

DOI

10.12913/22998624/188656

• Języki publikacji: EN

Abstrakty

EN

In the domain of clinical imaging, the exact and quick identification proof of bone fractures assumes a crucial part in a pivotal role in facilitating timely and effective patient care. This research tends to this basic need by harnessing the force of profound learning, explicitly utilizing a Convolutional Neural Network (CNN) model as the foundation of our technique. The essential target of our study was to improve the mechanized recognition of bone fractures in X-ray images, utilizing the capacities of deep learning algorithms. The use of a CNN model permitted us to successfully capture and learn intricate patterns and features within the X-ray images, empowering the framework to make exact fracture detections. The training process included presenting the model to a various dataset, guaranteeing its versatility to an extensive variety of fracture types. The results of our research show the excellent performance of the CNN model in fracture detection, where our model has achieved an Average Precision 89.5%, Average Recall 87%, and the overall Accuracy 91%. These metrics assert the vigour of our methodology and highlight the capability of deep learning in medical image analysis.

Słowa kluczowe

EN

convolutional neural network; X-ray images; deep learning algorithm; medical image analysis

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2024
• Tom: Vol. 18, no 4
• Strony: 228--237
• Opis fizyczny: Bibliogr. 26 poz., fig., tab.

Twórcy

autor

Saad Aymen

• Department of Information Technology, Technical College of Management, Al-Furat Al-Awsat Technical University, Kufa, Iraq

• https://orcid.org/0000-0002-3582-6799

autor

Sheikh Usman Ullah

• Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia

• https://orcid.org/0000-0001-9054-093X

autor

Moslim Mortada Sabri

• Department of Medical Physics Sciences, Al-Mustaqbal University, Hilla, Iraq

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